Amorphous/polycrystalline NiMn selenide for high-performance supercapacitors.

Transition-metal selenides have been extensively studied as promising electrode materials for supercapacitors. Engineering amorphous/crystalline heterostructures is an effective strategy to improve rich active sites for accelerating redox reaction kinetics but still lacks exploration. In this study, an amorphous/crystalline heterostructure was designed and constructed by selenizing the self-sacrificial template NiMnS to generate amorphous Mn/polycrystalline Ni0.85Se-NiSe2 heterophase via the phase transformation from metal sulfide into metal selenide. The synergy of the complementary multi-components and amorphous/polycrystalline heterophase could enrich electron/ion-transport channels and expose abundant active sites, which accelerated electron/ion transfer and Faradaic reaction kinetics during charging/discharging. As expected, the optimal NiMnSe exhibited a high specific charge (1389.1 C g-1 at 1 A g-1), a good rate capability, and an excellent lifespan (88.9% retention). Moreover, the fabricated NiMnSe//activated carbon device achieved a long cycle life and energy density of 48.0 W h kg-1 at 800 W kg-1, shedding light on the potential for use in practical applications, such as electrochemical energy-storage devices.

CDC6, a key replication licensing factor, is overexpressed and confers poor prognosis in diffuse large B-cell lymphoma.

BACKGROUND: Cell division cycle 6 (CDC6) is a key licensing factor in the assembly of pre-replicative complexes at origins of replication. The role of CDC6 in the pathogenesis of in diffuse larger B-cell lymphoma (DLBCL) remains unknown. We aim to investigate the effects of CDC6 on the proliferation, apoptosis and cell cycle regulation in DLBCL cells, delineate its underlying mechanism, and to correlate CDC6 expression with clinical characteristics and prognosis of patients with DLBCL. METHODS: Initial bioinformatic analysis was performed to screen the potential role of CDC6 in DLBCL. Lentiviral constructs harboring CDC6 or shCDC6 was transfected to overexpress or knockdown CDC6 in SUDHL4 and OCI-LY7 cells. The cell proliferation was evaluated by CCK-8 assay, cell apoptosis was detected by Annexin-V APC/7-AAD double staining, and cell cycle was measured by flow cytometry. Real time quantitative PCR and western blot was used to characterize CDC6 expression and its downstream signaling pathways. The clinical data of DLBCL patients were retrospectively reviewed, the CDC6 expression in DLBCL or lymph node reactive hyperplasia tissues was evaluated by immunohistochemistry. RESULTS: In silico data suggest that CDC6 overexpression is associated with inferior prognosis of DLBCL. We found that CDC6 overexpression increased SUDHL4 or OCI-LY7 cell proliferation, while knockdown of CDC6 inhibited cell proliferation in a time-dependent manner. Upon overexpression, CDC6 reduced cells in G1 phase and did not affect cell apoptosis; CDC6 knockdown led to significant cell cycle arrest in G1 phase and increase in cell apoptosis. Western blot showed that CDC6 inhibited the expression of INK4, E-Cadherin and ATR, accompanied by increased Bcl-2 and deceased Bax expression. The CDC6 protein was overexpressed DLBCL compared with lymph node reactive hyperplasia, and CDC6 overexpression was associated with non-GCB subtype, and conferred poor PFS and OS in patients with DLBCL. CONCLUSION: CDC6 promotes cell proliferation and survival of DLBCL cells through regulation of G1/S cell cycle checkpoint and apoptosis. CDC6 is overexpressed and serves as a novel prognostic marker in DLBCL.

The protective role of NR4A3 in acute myocardial infarction by suppressing inflammatory responses via JAK2-STAT3/NF-κB pathway.

Inflammatory responses play a critical role in left ventricular remodeling after acute myocardial infarction (AMI). NR4A3, a member of the NR4A orphan nucleus receptor family, has recently emerged as a therapeutic target for treatment of inflammation. This aim of this study is to explore the therapeutic effect of NR4A3 in cardiac remodeling post AMI. Male C57BL/6 mice were administered with lentiviral over-expression of NR4A3 (lenti-NR4A3) or empty vector (lenti-con) 7 days before coronary artery ligation. H9c2 cardiomyocytes deprived of serum were used to mimic ischemic conditions in vivo. Lenti-NR4A3 treatment significantly repressed neutrophil infiltration in the myocardium, reduced infarct size, and attenuated the reduction of left ventricular function after AMI. Furthermore, NR4A3 over-expression inhibited the NF-κB (IκB) signaling by decreasing IκBα phosphorylation and by inhibiting the translocation of p65 to the nucleus. Meanwhile, NR4A3 over-expression also increases the activity of JAK2-STAT3 signaling in mouse hearts after AMI. The inhibitory effect of NR4A3 on NF-κB activation was almost completely abolished by the JAK2 inhibitor AG490, indicating that NR4A3 prevented serum deprivation induced NF-κB activation in a STAT3 dependent manner. These findings provide novel evidence that NR4A3 could inhibit post-AMI inflammation responses via JAK2-STAT3/NF-κB signaling and may well be a therapeutic target for cardiac remodeling after AMI.

Enhancing forensic investigations: Identifying bloodstains on various substrates through ATR-FTIR spectroscopy combined with machine learning algorithms.

The forensic analysis of bloodstains on various substrates plays a crucial role in criminal investigations. This study presents a novel approach for analyzing bloodstains using Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) in combination with machine learning. ATR-FTIR offers non-destructive and non-invasive advantages, requiring minimal sample preparation. By detecting specific chemical bonds in blood components, it enables the differentiation of various body fluids. However, the subjective interpretation of the spectra poses challenges in distinguishing different fluids. To address this, we employ machine learning techniques. Machine learning is extensively used in chemometrics to analyze chemical data, build models, and extract useful information. This includes both unsupervised learning and supervised learning methods, which provide objective characterization and differentiation. The focus of this study was to identify human and porcine blood on substrates using ATR-FTIR spectroscopy. The substrates included paper, plastic, cloth, and wood. Data preprocessing was performed using Principal Component Analysis (PCA) to reduce dimensionality and analyze latent variables. Subsequently, six machine learning algorithms were used to build classification models and compare their performance. These algorithms comprise Partial Least Squares Discriminant Analysis (PLS-DA), Decision Trees (DT), Logistic Regression (LR), Naive Bayes Classifier (NBC), Support Vector Machine (SVM), and Neural Network (NN). The results indicate that the PCA-NN model provides the optimal solution on most substrates. Although ATR-FTIR spectroscopy combined with machine learning effectively identifies bloodstains on substrates, the performance of different identification models still varies based on the type of substrate. The integration of these disciplines enables researchers to harness the power of data-driven approaches for solving complex forensic problems. The objective differentiation of bloodstains using machine learning holds significant implications for criminal investigations. This technique offers a non-destructive, simple, selective, and rapid approach for forensic analysis, thereby assisting forensic scientists and investigators in determining crucial evidence related to bloodstains.

Characterization of a 4 lncRNAs-based prognostic risk scoring system in adults with acute myeloid leukemia.

PURPOSE: The study aims to develop a prognostic scoring system based on prognostic lncRNAs for acute myeloid leukemia (AML). METHODS: Based on lncRNA expression profiles downloaded from The Cancer Genome Atlas (TCGA), differentially expressed long noncoding RNAs (DELs) between good prognosis and bad prognosis samples were screened, from which prognosis-related lncRNAs were selected using uni-variate and multi-variate Cox regression analysis. Based on the expression profiles of these signature prognosis-related lncRNAs, a risk scoring system was developed and applied to a training set and validated on a testing set. With sample-matched mRNAs of the signature lncRNAs, lncRNA-mRNA networks were built, followed by function analysis for the mRNAs in these networks. RESULT: Total 66 DELs were identified between good prognosis and bad prognosis samples. Among these DELs, LINC01003, CTD-2234N14, RP1-137K24, and RP11-834C111 were found to be independent predictors of prognosis. A risk scoring system based on the expressions of the 4 signature lncRNAs was developed. Kaplan-Meier survival analysis found that the risk score system could classify patients into high-risk and low-risk groups with significantly different survival outcomes. Function analysis showed that the mRNAs in these lncRNA-mRNA networks were significantly linked to mTOR signaling pathway, apoptosis, Fc epsilon RI signaling pathway, B cell receptor signaling pathway, natural killer cell mediated cytotoxicity, and T cell receptor signaling pathway. CONCLUSION: This study suggested a promising 4 prognostic lncRNAs-based risk scoring system in AML. These 4 lncRNAs may play roles in regulating prognosis partly via mTOR signaling pathway, apoptosis, and some immune-related pathways.